1. Technical Field
This disclosure relates to camera pose estimation and dense reconstruction from a video.
2. Description of Related Art
In computer vision, Structure-from-Motion (SFM) approaches may be used to infer camera poses (positions and orientations) from 2D images. See Richard Hartley and Andrew Zisserman, “Multiple view geometry in computer vision”, volume 2. Cambridge Univ. Press, 2000.
This problem may be solved using offline SFM approaches. See Sameer Agarwal, Yasutaka Furukawa, Noah Snavely, Ian Simon, Brian Curless, Steven M Seitz, and Richard Szeliski, “Building rome in a day”, Communications of the ACM, 54(10):105-112, 2011, and Changchang Wu, “Towards linear-time incremental structure from motion”, In 3DV, 2013. However, these approaches may be very time-consuming. Offline SFM approaches may infer the camera position and orientation for each image using pairwise 2D-2D image point correspondences after collecting all images. The camera positions and orientations may be refined with Bundle Adjustment operation. See Bill Triggs, Philip McLauchlan, Richard Hartley, and Andrew Fitzgibbon, “Bundle adjustment—a modern synthesis”, Vision algorithms: theory and practice, pages 153-177, 2000). But this may also be computationally heavy.
Online SFM approaches may operate sequentially from a video stream and infer camera position and orientation for each provided image sequentially before later images arrive. However, these online SFM approaches may be inaccurate with large errors in the estimated camera pose and orientation due to errors accumulated along the process. See Georg Klein and David Murray “Parallel tracking and mapping for smaller workspaces”, In ISMAR, 2007; Georg Klein and David Murray, “Parallel tracking and mapping on a camera phone”, In ISMAR, 2009.